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Computes the cross-entropy loss between true labels and predicted labels.

Inherits From: `Loss`

tf.keras.losses.BinaryCrossentropy( from_logits=False, label_smoothing=0, reduction=losses_utils.ReductionV2.AUTO, name='binary_crossentropy' )

Use this cross-entropy loss when there are only two label classes (assumed to be 0 and 1). For each example, there should be a single floating-point value per prediction.

In the snippet below, each of the four examples has only a single floating-pointing value, and both `y_pred`

and `y_true`

have the shape `[batch_size]`

.

y_true = [[0., 1.], [0., 0.]] y_pred = [[0.6, 0.4], [0.4, 0.6]] # Using 'auto'/'sum_over_batch_size' reduction type. bce = tf.keras.losses.BinaryCrossentropy() bce(y_true, y_pred).numpy() 0.815

# Calling with 'sample_weight'. bce(y_true, y_pred, sample_weight=[1, 0]).numpy() 0.458

# Using 'sum' reduction type. bce = tf.keras.losses.BinaryCrossentropy( reduction=tf.keras.losses.Reduction.SUM) bce(y_true, y_pred).numpy() 1.630

# Using 'none' reduction type. bce = tf.keras.losses.BinaryCrossentropy( reduction=tf.keras.losses.Reduction.NONE) bce(y_true, y_pred).numpy() array([0.916 , 0.714], dtype=float32)

Usage with the `tf.keras`

API:

model.compile(optimizer='sgd', loss=tf.keras.losses.BinaryCrossentropy())

Args | |
---|---|

`from_logits` | Whether to interpret `y_pred` as a tensor of logit values. By default, we assume that `y_pred` contains probabilities (i.e., values in [0, 1]). **Note - Using from_logits=True may be more numerically stable. |

`label_smoothing` | Float in [0, 1]. When 0, no smoothing occurs. When > 0, we compute the loss between the predicted labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5. Larger values of `label_smoothing` correspond to heavier smoothing. |

`reduction` | (Optional) Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO` . `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE` . When used with `tf.distribute.Strategy` , outside of built-in training loops such as `tf.keras` `compile` and `fit` , using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training tutorial for more details. |

`name` | (Optional) Name for the op. Defaults to 'binary_crossentropy'. |

`from_config`

@classmethod from_config( config )

Instantiates a `Loss`

from its config (output of `get_config()`

).

Args | |
---|---|

`config` | Output of `get_config()` . |

Returns | |
---|---|

A `Loss` instance. |

`get_config`

get_config()

Returns the config dictionary for a `Loss`

instance.

`__call__`

__call__( y_true, y_pred, sample_weight=None )

Invokes the `Loss`

instance.

Args | |
---|---|

`y_true` | Ground truth values. shape = `[batch_size, d0, .. dN]` , except sparse loss functions such as sparse categorical crossentropy where shape = `[batch_size, d0, .. dN-1]` |

`y_pred` | The predicted values. shape = `[batch_size, d0, .. dN]` |

`sample_weight` | Optional `sample_weight` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]` , then the total loss for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each loss element of `y_pred` is scaled by the corresponding value of `sample_weight` . (Note on`dN-1` : all loss functions reduce by 1 dimension, usually axis=-1.) |

Returns | |
---|---|

Weighted loss float `Tensor` . If `reduction` is `NONE` , this has shape `[batch_size, d0, .. dN-1]` ; otherwise, it is scalar. (Note `dN-1` because all loss functions reduce by 1 dimension, usually axis=-1.) |

Raises | |
---|---|

`ValueError` | If the shape of `sample_weight` is invalid. |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/losses/BinaryCrossentropy